Calidad en el desarrollo de software desde una perspectiva bibliométrica en el periodo 2018 – 2022

The research shows the bibliometric analysis of software engineering, a situation that arises from the need to understand how the development process has been evolving and how companies have been adopting, adapting, and standardizing it for the development of quality software. The objective of this work is to carry out a bibliometric study of the scientific production of quality-oriented software engineering, whose results will be the basis for future research. The methodology used was descriptive - retrospective, being the data collected from the Scopus database; For its collection and subsequent analysis, several parameters were used to define the inclusion and exclusion criteria, obtaining 144 resulting articles; in addition, multiple questions were raised that allowed visualizing the main trends through their descriptive analysis.  At the end of the process, the IEE Access journal with the most published articles was determined, Mkaouer Mohamed Wiem as the most relevant author on the subject, the United States as the country with the highest number of publications, among other indicators. In addition, the importance of this type of research and the preferences of researchers were evaluated, all evidenced by the number of publications that have been developed in the period 2018 – 2022.La investigación muestra el análisis bibliométrico de la ingeniería de software, situación que aparece de la necesidad de entender cómo ha ido evolucionando el proceso de desarrollo y cómo las empresas lo han ido adoptando, adaptando y estandarizando para la construcción de software de calidad. El objetivo del trabajo es realizar un estudio bibliométrico de la producción científica de la ingeniería de software orientada a la calidad, cuyos resultados sean base para futuras investigaciones. La metodología utilizada fue descriptiva – retrospectiva, siendo los datos recolectados a partir de la base de datos Scopus; para su obtención y posterior análisis, se utilizaron varios parámetros que definieron los criterios de inclusión y exclusión, obteniéndose 144 artículos resultantes; además, se plantearon múltiples interrogantes que permitieron visualizar las principales tendencias a través del análisis descriptivo de los mismos. Al final del proceso, se determinó la revista IEE Access con más artículos publicados, Mkaouer Mohamed Wiem como el autor más relevante en el tema, Estados Unidos como el país con el mayor número de publicaciones, entre otros indicadores. Además, se evaluó la importancia de este tipo de investigaciones y las preferencias de los investigadores, todo ello, evidenciado por el número de publicaciones que se han desarrollado en el periodo 2018 – 2022.

A Hybrid Multi-Filter Wrapper Feature Selection Method for Software Defect Predictors

Software Defect Prediction (SDP) is an approach used for identifying defect-prone software modules or components. It helps software engineer to optimally, allocate limited resources to defective software modules or components in the testing or maintenance phases of software development life cycle (SDLC). Nonetheless, the predictive performance of SDP models reckons largely on the quality of dataset utilized for training the predictive models. The high dimensionality of software metric features has been noted as a data quality problem which negatively affects the predictive performance of SDP models. Feature Selection (FS) is a well-known method for solving high dimensionality problem and can be divided into filter-based and wrapper-based methods. Filter-based FS has low computational cost, but the predictive performance of its classification algorithm on the filtered data cannot be guaranteed. On the contrary, wrapper-based FS have good predictive performance but with high computational cost and lack of generalizability. Therefore, this study proposes a hybrid multi-filter wrapper method for feature selection of relevant and irredundant features in software defect prediction. The proposed hybrid feature selection will be developed to take advantage of filter-filter and filter-wrapper relationships to give optimal feature subsets, reduce its evaluation cycle and subsequently improve SDP models overall predictive performance in terms of Accuracy, Precision and Recall values

Software Defect Prediction Using Neural Network Based SMOTE

Software defect prediction is a practical approach to improve the quality and efficiency of time and costs for software testing by focusing on defect modules. The defect prediction software dataset naturally has a class imbalance problem with very few defective modules compared to non-defective modules. Class imbalance can reduce performance from classification. In this study, we applied the Neural Networks Based Synthetic Minority Over-sampling Technique (SMOTE) to overcome class imbalances in the six NASA datasets. Neural Network based on SMOTE is a combination of Neural Network and SMOTE with each hyperparameters that are optimized using random search. The results use a nested 5-cross validation show increases Bal by 25.48% and Recall by 45.99% compared to the original Neural Network. We also compare the performance of Neural Network based SMOTE with SMOTE + Traditional Machine Learning Algorithm. The Neural Network based SMOTE takes first place in the average rank

Model Balanced Bagging Berbasis Decision Tree Pada Dataset Imbalanced Class

Algoritma klasifikasi merupakan algoritma yang sangat sering digunakan beriringan dengan kebutuhan manusia, namun peneliti an sebelumnya sering dijumpai kendala saat menggunakan algoritma klasifikasi. Salah satu permasalahan yang sering sekali dijumpai ialah kasus imbalanced dataset. Sehingga dalam penelitian ini diusulkan ensemble method untuk mengatasinya, salah satu algoritma ensemble method yang terkenal ialah bagging. Implementasi balanced-bagging digunakan untuk meningkatkan kemampuan dari algoritma bagging. Dalam penelitian ini melibatkan perbandingan tiga model klasifikasi berbeda dengan lima dataset yang memiliki imbalanced ratio (IR) yang berbeda, Model akan dievaluasi berdasarkan metrik akurasi (balanced accuracy), geometric mean dan area under curve (AUC). Model pertama merupakan proses klasifikasi menggunakan Decision Tree (tanpa Bagging),  Model kedua merupakan proses klasifikasi menggunakan Decision Tree (dengan Bagging) dan model ketiga menggunakan Decision Tree (dengan Balanced-Bagging). Implementasi metode bagging dan balanced bagging terhadap algoritma klasifikasi Decision Tree mampu meningkatkan kinerja hasil akurasi (balanced accuracy), geometric mean, dan AUC. Secara umum model Decision Tree + Balanced Bagging menghasilkan kinerja yang terbaik pada seluruh dataset yang digunakan